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In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

Objective: This study was done for comparative study of dexmedetomidine with that o tramadol and pethidine in the treatment of post-neuraxial anaesthesia shivering. Design: This was a prospective, randomised, double-blinded study. Setting And Subjects: Hundred patients of both genders, aged 18–70 years with American Society of Anesthesiologists physical status I and II undergoing neuraxial a( spinal or combined spinal and epidural)anaesthesia for elective surgery were enrolled in this study. Sixty of them developed shivering after an intrathecal injection of 0.5% hyperbaric bupivacaine 15 mg. They were then randomly allocated to receive either intravenous dexmedetomidine 0.5 μg/kg, pethidine 0.5 mg/kg or tramadol 0.5 mg/kg. Outcome measures: The response rate to treatment, the degree of sedation and the side-effects were recorded. Results: The response rate to treatment was highest in the dexmedetomidine group, and it was only signicant when compared to tramadol group (p = 0.0012). It was noted that the response rate was higher in the pethidine than in the tramadol group. This difference was not statistically signicant (p = 0.082). The sedation score post treatment was similar in all three groups, but more patients in the dexmedetomidine group developed hypotension and bradycardia (p< 0.05). Conclusion: Dexmedetomidine 0.5 μg/ml was more effective than tramadol 0.5 mg/ml and pethidine 0.5 mg/ml, and both tramadol and pethidine were found to have similar efcacy, in the treatment of post-neuraxial anaesthesia shivering. However, dexmedetomidine caused a higher incidence of hypotension and bradycardia.

Topical lidocaine patches are commonly used to relieve pain and suffering in various clinical and household settings. Despite its extensive use, excessive skin absorption during numbing or pain reduction procedures can cause systemic toxicity, which can lead to life-threatening conditions. Rapid and reliable monitoring of escalating levels of lidocaine in the blood could help management/prevention of lidocaine overdose and its associated complications. To address this need, here we have developed a disposable point-of-care (POC) diagnostic platform composed of an integrated graphene-based electrochemical sensor with paper-based microfluidics for rapid detection of lidocaine levels in serum and blood samples. The fabrication process takes advantage of advanced, scalable manufacturing techniques, including printing, laser processing, and nondestructive near infrared (NIR) drying. The sensitivity tests of the platform revealed a sensitivity of ∼0.2 μA μM

The growing pervasiveness of opioid-based drugs such as fentanyl and its analogs represent a foremost hazard to the civilian population and burden on the first responders and clinicians. Thus, to enable a rapid and low-cost surveillance system to detect fentanyl in a non-ideal environment, we demonstrate the use of laser-induced nano-porous carbon structures directly onto commercially available polyimide sheets for rapid and cost-effective manufacturing of electrochemical sensors for fentanyl detection. The porous carbon surface instigated by various laser energy densities was analyzed towards morphological, vibrational, and fentanyl sensing properties. The results showed that laser carbonized electrode (LCE) prepared with 31 J/cm

The high prevalence of iron deficiency anemia and low compliance of iron and folic acid supplementation was reported among pregnant women in Nepal. This study aimed to determine the knowledge and compliance of iron and folic acid supplementation among pregnant and postnatal women in Kathmandu. A descriptive cross-sectional study design was adopted for the study. Sample size was 103 and purposive sampling technique was used to collect the data. All Pregnant women of having above 7 month pregnancy and postnatal’s up to 6 months women were included. Data was collected by using self-administered questionnaire .All the obtained data were analyzed with the help of SPSS version 20. Assessment of knowledge revealed that 85.6 % (88) of the participants have adequate knowledge regarding iron and folic acid supplementation .The findings showed 78.6% (81) of the respondents had iron and folic acid compliance as standard of Nepal government that is to be taken minimum 180 tablet before delivery. It concludes that the level of knowledge and compliance rate of iron and folic acid supplementation in Kathmandu is satisfactory in comparisons to national status i.e. 42% according to Nepal Demographic health survey 2016. Pearson chi-square test shows that level of knowledge on iron and folic acid tablet is not associated with the compliance of iron and folic acid tablet (χ²= 0.019, p< 0.889). The main reason of respondents having compliance is due to having proper counseling from health worker and non compliance is due to forgetting to have tablet. Therefore improving knowledge level only can’t improve compliance level, various factors like counseling, accessibility and availability of iron and folic acid tablet, side effect of tablet and family support also affects the compliance of iron and folic acid supplementation.

BACKGROUND The recent emergence of COVID-19 has caused an immense global public health crisis. The etiological agent of COVID-19 is the novel coronavirus SARS-CoV-2. More research in the field of developing effective vaccines against this emergent viral disease is indeed a need of the hour. OBJECTIVE The aim of this study was to identify effective vaccine candidates that can offer a new milestone in the battle against COVID-19. METHODS We used a reverse vaccinology approach to explore the SARS-CoV-2 genome among strains prominent in India. Epitopes were predicted and then molecular docking and simulation were used to verify the molecular interaction of the candidate antigenic peptide with corresponding amino acid residues of the host protein. RESULTS A promising antigenic peptide, GVYFASTEK, from the surface glycoprotein of SARS-CoV-2 (protein accession number QIA98583.1) was predicted to interact with the human major histocompatibility complex (MHC) class I human leukocyte antigen (HLA)-A*11-01 allele, showing up to 90% conservancy and a high antigenicity value. After vigorous analysis, this peptide was predicted to be a suitable epitope capable of inducing a strong cell-mediated immune response against SARS-CoV-2. CONCLUSIONS These results could facilitate selecting SARS-CoV-2 epitopes for vaccine production pipelines in the immediate future. This novel research will certainly pave the way for a fast, reliable, and effective platform to provide a timely countermeasure against this dangerous virus responsible for the COVID-19 pandemic.

Background The recent emergence of COVID-19 has caused an immense global public health crisis. The etiological agent of COVID-19 is the novel coronavirus SARS-CoV-2. More research in the field of developing effective vaccines against this emergent viral disease is indeed a need of the hour. Objective The aim of this study was to identify effective vaccine candidates that can offer a new milestone in the battle against COVID-19. Methods We used a reverse vaccinology approach to explore the SARS-CoV-2 genome among strains prominent in India. Epitopes were predicted and then molecular docking and simulation were used to verify the molecular interaction of the candidate antigenic peptide with corresponding amino acid residues of the host protein. Results A promising antigenic peptide, GVYFASTEK, from the surface glycoprotein of SARS-CoV-2 (protein accession number QIA98583.1) was predicted to interact with the human major histocompatibility complex (MHC) class I human leukocyte antigen (HLA)-A*11-01 allele, showing up to 90% conservancy and a high antigenicity value. After vigorous analysis, this peptide was predicted to be a suitable epitope capable of inducing a strong cell-mediated immune response against SARS-CoV-2. Conclusions These results could facilitate selecting SARS-CoV-2 epitopes for vaccine production pipelines in the immediate future. This novel research will certainly pave the way for a fast, reliable, and effective platform to provide a timely countermeasure against this dangerous virus responsible for the COVID-19 pandemic.

The concept of rainfall-runoff variation is a non-linear event and highly tedious and continuously changing process. It involves different parameters which include i.e. mainly rainfall, soil, morphology and vegetation. The modelling for runoff prediction requires many engineering applications. The biggest obstacles in prediction of runoff are the estimation of extreme values. Unless models are not able to get the dynamics of rainfall-runoff process accurately, accurate prediction of these extremes is not possible. Various approaches have been adopted to represent rainfall-runoff process. The best among those approaches is using Artificial Neural Network techniques for the development of long -term and short-term forecasting models. Comparision is held between various approaches for runoff forecasting using ANNs., {"references":["Singh, P. V., Kumar Akhilesh, Rawat J. S., & Kumar Devendra, Artificial Neural Networks based daily rainfall-runoff model for an agricultural hilly watershed, International Journal of Engineering and Management Sciences","Christian W. Dawson & Robert Wilby, An Artificial Neural Network approach to rainfall-runoff modelling, Hydrological Sciences Journal","Satanand Mishra, Prince Gupta, S. K. Pandey & J. P. Shukla, An Efficient approach of Artificial Neural Network in runoff forecasting, International Journal of Computer Applications","Yonas B. Dibike & Dimitri P. Solomatine, Riverflow forecasting using Artificial Neural Networks, EGS Journal of Physics and Chemistry of the Earth","Gallant, A. R., and H. White, On learning the derivatives of an unknown mapping with multilayer feedforward networks, Neural Networks, 5, 129-138, 1992","Yojna Arora, Abhishek Singhal, Abhay Bansal, A Study of Applications of RBF Network, International Journal of Computer Applications","Yogesh Shirke, Prof. Dr. Rameshwar Kawitkar, Mr. Selva , Artificial Neural Network based Runoff Prediction Model for a Reservoir,International Journal of Engineering Research & Technology (IJERT)"]}